Wind energy

Intro to wind energy

Exercise 1

Map wind turbines in Rogaland from OSM and calculate the total energy capacity installed.

Objetives

OSM (and how get data from it)
Map points data (interactive map)
Manipulate spatial data (e.g., intersection points - polygons)
Types of data in a data frame
Data wrangling

Solution

Load libraries

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library(osmdata)
library(sf)
library(tmap)
library(tidyverse)

Get data from OpenStreetMap with osmdata, and transform to a sf object.

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query <- opq(bbox = "Rogaland", timeout = 50) |> 
   add_osm_feature(key = "generator:source", value = "wind")
data <-  osmdata_sf(query)

wind_turbines <- data$osm_points

Names of the data frame,

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wind_turbines |>
  names()

 [1] "osm_id"                           "name"                            
 [3] "aeroway:light"                    "aeroway:light:character"         
 [5] "aeroway:light:colour"             "aeroway:light:icao_type"         
 [7] "aeroway:light:intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator:method"                
[13] "generator:output:electricity"     "generator:source"                
[15] "generator:type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref:hinder"                      
[23] "ref:kystverket"                   "seamark:landmark:category"       
[25] "seamark:landmark:conspicuity"     "seamark:landmark:vertical_length"
[27] "seamark:light:1:character"        "seamark:light:1:colour"          
[29] "seamark:light:1:group"            "seamark:light:1:height"          
[31] "seamark:light:1:multiple"         "seamark:light:1:period"          
[33] "seamark:light:1:range"            "seamark:light:2:category"        
[35] "seamark:light:2:character"        "seamark:light:2:colour"          
[37] "seamark:name"                     "seamark:type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"

Clean names

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wind_turbines <- wind_turbines |>
  janitor::clean_names()

names(wind_turbines)

 [1] "osm_id"                           "name"                            
 [3] "aeroway_light"                    "aeroway_light_character"         
 [5] "aeroway_light_colour"             "aeroway_light_icao_type"         
 [7] "aeroway_light_intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator_method"                
[13] "generator_output_electricity"     "generator_source"                
[15] "generator_type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref_hinder"                      
[23] "ref_kystverket"                   "seamark_landmark_category"       
[25] "seamark_landmark_conspicuity"     "seamark_landmark_vertical_length"
[27] "seamark_light_1_character"        "seamark_light_1_colour"          
[29] "seamark_light_1_group"            "seamark_light_1_height"          
[31] "seamark_light_1_multiple"         "seamark_light_1_period"          
[33] "seamark_light_1_range"            "seamark_light_2_category"        
[35] "seamark_light_2_character"        "seamark_light_2_colour"          
[37] "seamark_name"                     "seamark_type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"

Interactive map with tmap.

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tmap_mode("view")

tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")

Figure 1: Wind turbines in Rogaland (Data from OSM)

There are some wind turbines that ar not in Rogaland, so we are going to delect them from the dataset. For that we need to intersect our points (wind turbines) with the polygon (Rogaland).

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# Get Norwegian counties (polygons)) from GISCO
counties <- giscoR::gisco_get_nuts(country = "NO",
                                   year = "2021",
                                   nuts_level = 3,
                                   epsg = "4326",
                                   resolution = "01") 
# Get only rogaland county
rogaland <- counties |> 
  filter(NUTS_NAME == "Rogaland")

# Intersect wind turbines (points) with rogaland (polygons)
wind_turbines <- wind_turbines |> 
  st_intersection(rogaland)

# Plot
tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")

Figure 2: Wind turbines in Rogaland (Data from OSM)

Now, we are going to calculate the maximum capacity (MW) installed in the region. If we inspect the data, there is a column describing called generator:output:electricity, which give the information we are looking for in MW. However, if we inspect the columns we see that the values are characters (<chr>) not numbers, so we can not carried out numerical operations on them.

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glimpse(wind_turbines)

Rows: 256
Columns: 51
$ osm_id                           <chr> "1277731149", "1510248163", "15102482…
$ name                             <chr> "Høg-Jæren energipark 31", "Høg-Jæren…
$ aeroway_light                    <chr> "obstacle", NA, "obstacle", "obstacle…
$ aeroway_light_character          <chr> "flashing", NA, "flashing", "flashing…
$ aeroway_light_colour             <chr> "red", NA, "red", "red", "red", "red"…
$ aeroway_light_icao_type          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ aeroway_light_intensity          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brand                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ date_constructed                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ description                      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ele                              <chr> "264", "268", "250", "234", "205", "2…
$ generator_method                 <chr> "wind_turbine", "wind_turbine", "wind…
$ generator_output_electricity     <chr> "2.3 MW", "2.3 MW", "2.3 MW", "2.3 MW…
$ generator_source                 <chr> "wind", "wind", "wind", "wind", "wind…
$ generator_type                   <chr> "horizontal_axis", "horizontal_axis",…
$ height                           <chr> "126", "126", "126", "126", "89", "89…
$ man_made                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ model                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ operator                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ power                            <chr> "generator", "generator", "generator"…
$ ref                              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ref_hinder                       <chr> "70647", "70646", "70640", "71472", "…
$ ref_kystverket                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_category        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_conspicuity     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_vertical_length <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_group            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_height           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_multiple         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_period           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_range            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_name                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_type                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ serial_number                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ source                           <chr> "Kartverket Luftfartshindre", "Kartve…
$ NUTS_ID                          <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ LEVL_CODE                        <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
$ URBN_TYPE                        <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CNTR_CODE                        <chr> "NO", "NO", "NO", "NO", "NO", "NO", "…
$ NAME_LATN                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ NUTS_NAME                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ MOUNT_TYPE                       <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ COAST_TYPE                       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ FID                              <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ geo                              <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ geometry                         <POINT [°]> POINT (5.792002 58.65093), POIN…

Therefore, we need to transform the data to numbers. We can use the function parse_number() from the package readr (inside tidyverse). For not deleting the column, we can generate a new one (e.g., gen_electricity_mw)

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wind_turbines <- wind_turbines |> 
  mutate(gen_electricity_mw = parse_number(generator_output_electricity) )
     
# Show first 10 values of the column
wind_turbines$gen_electricity_mw |> 
  head(10)

 [1] 2.3 2.3 2.3 2.3  NA  NA 2.3 2.3 2.3 2.3

Remove NA in energy generator:

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wind_turbines <- wind_turbines |> 
  drop_na(gen_electricity_mw)

# Summary of energy generator (without NA) 
wind_turbines$gen_electricity_mw |> 
  head(10)

 [1] 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

calculate the total generation energy capacity.

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total_capacity_mw <- wind_turbines$gen_electricity_mw |> sum()

print(total_capacity_mw)

[1] 905.3

So the total wind capacity installed in Rogaland is 905.3 MW. Note, that this is only the maximum energy installed and not how much it is generate in the region. It does not take into account the efficiency of the turbines nor wind availability!!.

Exercise 2

Map wind farms and wind turbines with NVE data. Free but need to be download before reading into R (save it in a folder: e.g., ~/data/big_data/NVE/NVEData). I have download them in .geojson format. Therefore we need to read them with the geojsonsf package, which converts GeoJSON to an sfobjects.

Objetives

Formats of spatial data
Types of vector data
Plot more that one layer in a interactive map
Load data from files
Data wrangling

Solution

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# Libraries

library(geojsonsf)
library(sf)
library(tidyverse)
library(tmap)

Load data

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wind_turbines_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_Vindturbin.geojson"
wind_turbines_nve <- geojson_sf(wind_turbines_nve_path)
wind_turbines_nve

Simple feature collection with 272 features and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4.905257 ymin: 58.31454 xmax: 6.51592 ymax: 59.41634
Geodetic CRS:  WGS 84
First 10 features:
   objektType   saksTittel saksKategori dataUttaksdato              eksportType
1  Vindturbin      Svåheia            2       20240207 NVEs nedlastningsløsning
2  Vindturbin       Tysvær            2       20240207 NVEs nedlastningsløsning
3  Vindturbin    Høg-Jæren            2       20240207 NVEs nedlastningsløsning
4  Vindturbin       Tysvær            2       20240207 NVEs nedlastningsløsning
5  Vindturbin    Bjerkreim            2       20240207 NVEs nedlastningsløsning
6  Vindturbin Vardafjellet            2       20240207 NVEs nedlastningsløsning
7  Vindturbin    Bjerkreim            2       20240207 NVEs nedlastningsløsning
8  Vindturbin   Måkaknuten            2       20240207 NVEs nedlastningsløsning
9  Vindturbin       Utsira            2       20240207 NVEs nedlastningsløsning
10 Vindturbin     Tellenes            2       20240207 NVEs nedlastningsløsning
                    geometry
1  POINT (6.091838 58.38779)
2  POINT (5.552303 59.31521)
3   POINT (5.76918 58.64514)
4   POINT (5.557336 59.2931)
5  POINT (5.934883 58.59081)
6  POINT (5.894319 58.83959)
7  POINT (5.945595 58.58838)
8  POINT (5.966087 58.68458)
9  POINT (4.906018 59.31497)
10 POINT (6.514851 58.34872)

Show the code

wind_farms_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_VindkraftanleggOmr.geojson"
wind_farms_nve <- geojson_sf(wind_farms_nve_path)
wind_farms_nve

Simple feature collection with 57 features and 14 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4.890143 ymin: 58.2376 xmax: 6.520988 ymax: 59.61306
Geodetic CRS:  WGS 84
First 10 features:
   dataUttaksdato totaltAntTurbiner    objektType utAvDriftDato idriftDato
1        20240207              <NA> Vindkraftverk          <NA>       <NA>
2        20240207              <NA> Vindkraftverk          <NA>       <NA>
3        20240207              <NA> Vindkraftverk          <NA>       <NA>
4        20240207              <NA> Vindkraftverk          <NA>       <NA>
5        20240207                37 Vindkraftverk          <NA>   20191219
6        20240207              <NA> Vindkraftverk          <NA>       <NA>
7        20240207              <NA> Vindkraftverk          <NA>       <NA>
8        20240207              <NA> Vindkraftverk          <NA>       <NA>
9        20240207              <NA> Vindkraftverk          <NA>       <NA>
10       20240207              <NA> Vindkraftverk          <NA>       <NA>
   status fylkesnavn
1       V   Rogaland
2       V   Rogaland
3       V   Rogaland
4      FJ   Rogaland
5       D   Rogaland
6      FJ   Rogaland
7      FJ   Rogaland
8       V   Rogaland
9       V   Rogaland
10     FJ   Rogaland
                                                                    saksLenke
1  https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=194
2   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=29
3   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=36
4   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=52
5   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=32
6   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=37
7   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=64
8   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=55
9   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=53
10 https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=120
   forventetProduksjon_Gwh kommunenavn                       geometry
1                     34.0   Stavanger POLYGON ((5.616381 59.07174...
2                    408.0   Bjerkreim POLYGON ((5.892451 58.63726...
3                    785.4   Bjerkreim POLYGON ((5.893369 58.70034...
4                    272.0        Lund POLYGON ((6.26167 58.56276,...
5                    558.4   Bjerkreim POLYGON ((5.995785 58.58672...
6                    306.0   Eigersund POLYGON ((6.206857 58.56671...
7                    306.0     Sokndal POLYGON ((6.153781 58.36661...
8                    544.0     Gjesdal POLYGON ((6.293888 58.82249...
9                    265.2   Bjerkreim POLYGON ((6.07609 58.68459,...
10                   204.0      Karmøy POLYGON ((5.29149 59.23401,...
   saksKategori               tiltakshaver effekt_MW              eksportType
1             2 MARIN ENERGI TESTSENTER AS      10.0 NVEs nedlastningsløsning
2             3         LYSE PRODUKSJON AS     120.0 NVEs nedlastningsløsning
3             3         LYSE PRODUKSJON AS     231.0 NVEs nedlastningsløsning
4             4          BJERKREIM VIND AS      80.0 NVEs nedlastningsløsning
5             2          BJERKREIM VIND AS     159.1 NVEs nedlastningsløsning
6             4          BJERKREIM VIND AS      90.0 NVEs nedlastningsløsning
7             4                  ZEPHYR AS      90.0 NVEs nedlastningsløsning
8             2     GILJA VINDKRAFTVERK AS     160.0 NVEs nedlastningsløsning
9             3                  ZEPHYR AS      78.0 NVEs nedlastningsløsning
10            4      EQUINOR WIND POWER AS      60.0 NVEs nedlastningsløsning

Map both together:

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tmap_mode("view")


tm_shape(wind_farms_nve) + 
  tm_fill("status", alpha = 0.5, title = "Wind farm status") +
  # Add wind turbines
  tm_shape(wind_turbines_nve) +
  tm_dots(col = "#0072B2")

Figure 3: Wind turbines in Rogaland (Data from NVE)

Status

D - Drift (Operations) N - Nedlagt (Decommissioned) O - Ombygd (Rebuilt) P - Planlagt (Planned) P1 - Planlagt illustrert (Planed illustrated) P2 - Planlagt, prosjektert (Planed, projected) U - Under arbeid (in progress) V - Vedtatt (Adopted) FJ - Fjernet (Removed)